# Machine Learning Applications in Misuse and Anomaly Detection

arXiv: https://arxiv.org/abs/2009.06709 · HTML: https://mlsanity.com/paper/2009.06709/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks](https://mlsanity.com/paper/2204.13814/) (2022)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Hybrid IDS Using Signature-Based and Anomaly-Based Detection](https://mlsanity.com/paper/2601.11998/) (2026)

## Abstract (arXiv)

Machine learning and data mining algorithms play important roles in designing intrusion detection systems. Based on their approaches toward the detection of attacks in a network, intrusion detection systems can be broadly categorized into two types. In the misuse detection systems, an attack in a system is detected whenever the sequence of activities in the network matches with a known attack signature. In the anomaly detection approach, on the other hand, anomalous states in a system are identified based on a significant difference in the state transitions of the system from its normal states. This chapter presents a comprehensive discussion on some of the existing schemes of intrusion detection based on misuse detection, anomaly detection and hybrid detection approaches. Some future directions of research in the design of algorithms for intrusion detection are also identified.